The depletion of conventional energy has resulted in utilization of non-conventional energy resources, such as wind, sunlight, tides, geothermal heat, etc. for generating energy and power. The renewable energy generated from these natural resources plays a significant role in meeting the energy requirements for constantly growing sectors in the global economy.
There has been tremendous growth in the utilization of wind energy for generating power in recent times. Market analysis at the end of 2011 indicates that wind power is growing at over 20% annually, with a worldwide installed capacity of 238,000 megawatts (MW), primarily in continents such as Europe, Asia, and the North America. Considering the impact of wind resources in the power market for delivering quality and sufficient quantity output, accurate forecast of wind resources is essential.
There have been several attempts made in the past for accurate forecast of wind resources. Several forecasting tools exists in the art that enable forecasting of wind resources based on different assumptions and concepts resulting in multiple alternate forecasts. Further, attempts have been made to combine these several alternate forecasts into a single forecast of superior accuracy using various statistical and machine learning methods.
One such method includes classifying, normalizing and grouping historic wind patterns and associating each group with an energy output that the wind farm produces using neural network and Bayesian logic. The method uses turbine specific data, met mast data and regional forecast information from external forecasting services to adaptively adjust its logic and update current wind patterns. The wind resource is forecast based on a match obtained from an historical database relating the updated wind pattern to wind farm energy and use of actual measured energy and turbine specific data. However, this method is limited to using only regional forecast information and ignoring turbine level forecasts. Further, performance of a neural network changes with change in parameters governing the network; and hence use of a single neural network may not cover the entire parameter space.
Another method that enables wind resource forecasting is implemented using two sub-systems. A wind forecasting subsystem of this method adaptively combines wide area wind forecast signals, alternate meteorological data sources and SCADA based inputs to produce a refined wide area wind forecast signal. This then acts as the input to another subsystem termed wind farm production forecasting, that uses turbine specific transfer functions and power curves to convert wide area wind forecast signals to turbine specific wind forecast signals and energy forecasts, respectively, that is further refined based on SCADA inputs. However, the adaptive statistics module employed by this method uses regional forecast information and ignores turbine specific forecasts.
Another technique for estimating wind resource forecasts utilizes an NWP model. The NWP model, in addition to receipt of wide area regional forecast as input, adjusts and calibrates its forecast based on turbine level measurements. However, the NWP model may not work well for short term forecasts. Further, the NWP model receives a single forecasting service as input and hence does not combine several forecasts. In addition, this technique is limited to application of physical models only.
A method to improve the accuracy of the NWP model for short term forecasting generates multiple forecasts from a single model by using slightly different initial conditions (and/or boundary conditions), which are later combined to give an ensemble forecast. However, this method uses a single NWP model; the input/runtime boundary conditions are perturbed to generate multiple results. Accuracy is hence bounded by the model's performance. Further, ensemble techniques currently used are mostly mathematical and hence do not involve any machine learning approach.
Another approach involves adaptively combining alternate forecasts by means of two methods: (1) linearly combining them with appropriate weights assigned; and (2) exponentially weighing and tracking the best predictor. The approach further involves selection of the best forecasts using exponential weighing. However, the weighing method has inherent limitations in that it fails to adapt to changes, especially when the best predictor constantly changes.
A need therefore exists for a method and system that enable adaptive forecasting of wind resources by combining several alternate forecasts and achieving maximized forecast accuracy. More particularly, there is a need in the art to converge on a most efficient method of combining that is independent of the nature of the predictors, whose forecasts are to be combined, applicable universally, not limited to a specific location, and able to function over a range of forecast time horizons.
A need also exists for a method and system that enable use of alternate forecasts at turbine level. Further, there is a need for a method and system that enable wind resources forecasting by creating variants of artificial neural networks that covers the entire parameter space rather than relying on a single neural network.